@inproceedings{yue-etal-2025-mmmu,
title = "{MMMU}-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark",
author = "Yue, Xiang and
Zheng, Tianyu and
Ni, Yuansheng and
Wang, Yubo and
Zhang, Kai and
Tong, Shengbang and
Sun, Yuxuan and
Yu, Botao and
Zhang, Ge and
Sun, Huan and
Su, Yu and
Chen, Wenhu and
Neubig, Graham",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.736/",
doi = "10.18653/v1/2025.acl-long.736",
pages = "15134--15186",
ISBN = "979-8-89176-251-0",
abstract = "This paper introduces MMMU-Pro, a robust version of the Massive Multi-discipline Multimodal Understanding and Reasoning (MMMU) benchmark. MMMU-Pro rigorously assesses multimodal models' true understanding and reasoning capabilities through a three-step process based on MMMU: (1) filtering out questions answerable by text-only models, (2) augmenting candidate options, and (3) introducing a vision-only input setting where questions are embedded within images. This setting challenges AI to truly ``see'' and ``read'' simultaneously, testing \textit{a core human cognitive skill of seamlessly integrating visual and textual information}. Results show that model performance is substantially lower on MMMU-Pro than on MMMU, ranging from 16.8{\%} to 26.9{\%} across models. We explore the impact of OCR prompts and Chain of Thought (CoT) reasoning, finding that OCR prompts have minimal effect while CoT generally improves performance. MMMU-Pro provides a more rigorous evaluation tool, closely mimicking real-world scenarios and offering valuable directions for future multimodal research."
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<abstract>This paper introduces MMMU-Pro, a robust version of the Massive Multi-discipline Multimodal Understanding and Reasoning (MMMU) benchmark. MMMU-Pro rigorously assesses multimodal models’ true understanding and reasoning capabilities through a three-step process based on MMMU: (1) filtering out questions answerable by text-only models, (2) augmenting candidate options, and (3) introducing a vision-only input setting where questions are embedded within images. This setting challenges AI to truly “see” and “read” simultaneously, testing a core human cognitive skill of seamlessly integrating visual and textual information. Results show that model performance is substantially lower on MMMU-Pro than on MMMU, ranging from 16.8% to 26.9% across models. We explore the impact of OCR prompts and Chain of Thought (CoT) reasoning, finding that OCR prompts have minimal effect while CoT generally improves performance. MMMU-Pro provides a more rigorous evaluation tool, closely mimicking real-world scenarios and offering valuable directions for future multimodal research.</abstract>
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%0 Conference Proceedings
%T MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark
%A Yue, Xiang
%A Zheng, Tianyu
%A Ni, Yuansheng
%A Wang, Yubo
%A Zhang, Kai
%A Tong, Shengbang
%A Sun, Yuxuan
%A Yu, Botao
%A Zhang, Ge
%A Sun, Huan
%A Su, Yu
%A Chen, Wenhu
%A Neubig, Graham
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F yue-etal-2025-mmmu
%X This paper introduces MMMU-Pro, a robust version of the Massive Multi-discipline Multimodal Understanding and Reasoning (MMMU) benchmark. MMMU-Pro rigorously assesses multimodal models’ true understanding and reasoning capabilities through a three-step process based on MMMU: (1) filtering out questions answerable by text-only models, (2) augmenting candidate options, and (3) introducing a vision-only input setting where questions are embedded within images. This setting challenges AI to truly “see” and “read” simultaneously, testing a core human cognitive skill of seamlessly integrating visual and textual information. Results show that model performance is substantially lower on MMMU-Pro than on MMMU, ranging from 16.8% to 26.9% across models. We explore the impact of OCR prompts and Chain of Thought (CoT) reasoning, finding that OCR prompts have minimal effect while CoT generally improves performance. MMMU-Pro provides a more rigorous evaluation tool, closely mimicking real-world scenarios and offering valuable directions for future multimodal research.
%R 10.18653/v1/2025.acl-long.736
%U https://aclanthology.org/2025.acl-long.736/
%U https://doi.org/10.18653/v1/2025.acl-long.736
%P 15134-15186
Markdown (Informal)
[MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark](https://aclanthology.org/2025.acl-long.736/) (Yue et al., ACL 2025)
ACL
- Xiang Yue, Tianyu Zheng, Yuansheng Ni, Yubo Wang, Kai Zhang, Shengbang Tong, Yuxuan Sun, Botao Yu, Ge Zhang, Huan Sun, Yu Su, Wenhu Chen, and Graham Neubig. 2025. MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 15134–15186, Vienna, Austria. Association for Computational Linguistics.